Book

Docker container is currently the only officially-supported way to
running PaddlePaddle. This is reasonable as Docker now runs on all
major operating systems including Linux, Mac OS X, and Windows.
Please be aware that you will need to change Dockers settings to make full use
of your hardware resource on Mac OS X and Windows.

image: A Docker image is a pack of software. It could contain one or more programs and all their dependencies. For example, the PaddlePaddle’s Docker image includes pre-built PaddlePaddle and Python and many Python packages. We can run a Docker image directly, other than installing all these software. We can type

docker images

to list all images in the system. We can also run

docker pull paddlepaddle/paddle:0.10.0rc2

to download a Docker image, paddlepaddle/paddle in this example,
from Dockerhub.com.

container: considering a Docker image a program, a container is a
“process” that runs the image. Indeed, a container is exactly an
operating system process, but with a virtualized filesystem, network
port space, and other virtualized environment. We can type

docker run paddlepaddle/paddle:0.10.0rc2

to start a container to run a Docker image, paddlepaddle/paddle in this example.

By default docker container have an isolated file system namespace,
we can not see the files in the host file system. By using volume,
mounted files in host will be visible inside docker container.
Following command will mount current dirctory into /data inside
docker container, run docker container from debian image with
command ls/data.

We package PaddlePaddle’s compile environment into a Docker image,
called the develop image, it contains all compiling tools that
PaddlePaddle needs. We package compiled PaddlePaddle program into a
Docker image as well, called the production image, it contains all
runtime environment that running PaddlePaddle needs. For each version
of PaddlePaddle, we release both of them. Production image includes
CPU-only version and a CUDA GPU version and their no-AVX versions.

We put the docker images on dockerhub.com. You can find the
latest versions under “tags” tab at dockerhub.com. If you are in
China, you can use our Docker image registry mirror to speed up the
download process. To use it, please replace all paddlepaddle/paddle in
the commands to docker.paddlepaddle.org/paddle.

Production images, this image might have multiple variants:

GPU/AVX：paddlepaddle/paddle:<version>-gpu

GPU/no-AVX：paddlepaddle/paddle:<version>-gpu-noavx

CPU/AVX：paddlepaddle/paddle:<version>

CPU/no-AVX：paddlepaddle/paddle:<version>-noavx

Please be aware that the CPU-only and the GPU images both use the
AVX instruction set, but old computers produced before 2008 do not
support AVX. The following command checks if your Linux computer
supports AVX:

if cat /proc/cpuinfo | grep -i avx;thenecho Yes;elseecho No;fi

To run the CPU-only image as an interactive container:

docker run -it --rm paddlepaddle/paddle:0.10.0rc2 /bin/bash

Above method work with the GPU image too – the recommended way is
using nvidia-docker.

This image has packed related develop tools and runtime
environment. Users and developers can use this image instead of
their own local computer to accomplish development, build,
releasing, document writing etc. While different version of paddle
may depends on different version of libraries and tools, if you
want to setup a local environment, you must pay attention to the
versions. The development image contains:

gcc/clang

nvcc

Python

sphinx

woboq

sshd

Many developers use servers with GPUs, they can use ssh to login to
the server and run dockerexec to enter the docker
container and start their work. Also they can start a development
docker image with SSHD service, so they can login to the container
and start work.

The above command will compile PaddlePaddle and create a Dockerfile for building production image. All the generated files are in the build directory. “WITH_GPU” controls if the generated production image supports GPU. “WITH_AVX” controls if the generated production image supports AVX. “WITH_TEST” controls if the unit test will be generated.

The second step is to run:

docker build -t paddle:prod -f build/Dockerfile ./build

The above command will generate the production image by copying the compiled PaddlePaddle program into the image.

The Jupyter Notebook is an open-source web application that allows
you to create and share documents that contain live code, equations,
visualizations and explanatory text in a single browser.

PaddlePaddle Book is an interactive Jupyter Notebook for users and developers.
We already exposed port 8888 for this book. If you want to
dig deeper into deep learning, PaddlePaddle Book definitely is your best choice.